
'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__ :			str											=		logging.get_logger(__name__)

UpperCAmelCase__ :			int											=		{
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :str   =  'roc_bert'





	def __init__(       self      :							int   ,				__magic_name__      :							Optional[int]=30522   ,				__magic_name__      :							Optional[Any]=768   ,				__magic_name__      :							Dict=12   ,				__magic_name__      :							int=12   ,				__magic_name__      :							Tuple=3072   ,				__magic_name__      :							Optional[int]="gelu"   ,				__magic_name__      :							List[Any]=0.1   ,				__magic_name__      :							Dict=0.1   ,				__magic_name__      :							Optional[Any]=512   ,				__magic_name__      :							List[Any]=2   ,				__magic_name__      :							Optional[Any]=0.02   ,				__magic_name__      :							Tuple=1E-12   ,				__magic_name__      :							List[Any]=True   ,				__magic_name__      :							List[Any]=0   ,				__magic_name__      :							List[str]="absolute"   ,				__magic_name__      :							Dict=None   ,				__magic_name__      :							List[Any]=True   ,				__magic_name__      :							Optional[Any]=True   ,				__magic_name__      :							Dict=768   ,				__magic_name__      :							int=910   ,				__magic_name__      :							Optional[Any]=512   ,				__magic_name__      :							int=24858   ,				__magic_name__      :							Dict=True   ,				**__magic_name__      :							Union[str, Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				vocab_size
				lowerCAmelCase__										=				max_position_embeddings
				lowerCAmelCase__										=				hidden_size
				lowerCAmelCase__										=				num_hidden_layers
				lowerCAmelCase__										=				num_attention_heads
				lowerCAmelCase__										=				intermediate_size
				lowerCAmelCase__										=				hidden_act
				lowerCAmelCase__										=				hidden_dropout_prob
				lowerCAmelCase__										=				attention_probs_dropout_prob
				lowerCAmelCase__										=				initializer_range
				lowerCAmelCase__										=				type_vocab_size
				lowerCAmelCase__										=				layer_norm_eps
				lowerCAmelCase__										=				use_cache
				lowerCAmelCase__										=				enable_pronunciation
				lowerCAmelCase__										=				enable_shape
				lowerCAmelCase__										=				pronunciation_embed_dim
				lowerCAmelCase__										=				pronunciation_vocab_size
				lowerCAmelCase__										=				shape_embed_dim
				lowerCAmelCase__										=				shape_vocab_size
				lowerCAmelCase__										=				concat_input
				lowerCAmelCase__										=				position_embedding_type
				lowerCAmelCase__										=				classifier_dropout
				super().__init__(pad_token_id=__magic_name__   ,				**__magic_name__				)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_    :			Union[str, Any]    =	{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			List[Any]    =	[
							    """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TapasForMaskedLM""",
							    """TapasForQuestionAnswering""",
							    """TapasForSequenceClassification""",
							    """TapasModel""",
							    """TapasPreTrainedModel""",
							    """load_tf_weights_in_tapas""",
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Optional[Any]    =	[
							    """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFTapasForMaskedLM""",
							    """TFTapasForQuestionAnswering""",
							    """TFTapasForSequenceClassification""",
							    """TFTapasModel""",
							    """TFTapasPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
							from .tokenization_tapas import TapasTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tapas import (
														    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TapasForMaskedLM,
														    TapasForQuestionAnswering,
														    TapasForSequenceClassification,
														    TapasModel,
														    TapasPreTrainedModel,
														    load_tf_weights_in_tapas,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_tapas import (
														    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTapasForMaskedLM,
														    TFTapasForQuestionAnswering,
														    TFTapasForSequenceClassification,
														    TFTapasModel,
														    TFTapasPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Any    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






